Kapellos Lab

Transcriptomic analyses have advanced the understanding of complex disease pathophysiology including chronic obstructive pulmonary disease (COPD). However, identifying relevant biologic causative factors has been limited by the integration of high dimensionality data. COPD is characterized by lung destruction and inflammation with smoke exposure being a major risk factor. To define novel biological mechanisms in COPD, we utilized unsupervised and supervised interpretable machine learning analyses of single cell-RNA sequencing data from the gold standard mouse smoke exposure model to identify significant latent factors (context-specific co-expression modules) impacting pathophysiology. The machine learning transcriptomic signatures coupled to protein networks uncovered a reduction in network complexity and novel biological alterations in actin-associated gelsolin (GSN), which was transcriptionally linked to disease state. GSN was altered in airway epithelial cells in the mouse model and in human COPD. GSN was increased in plasma from COPD patients, and smoke exposure resulted in enhanced GSN release from airway cells from COPD patients. This method provides insights into rewiring of transcriptional networks that are associated with COPD pathogenesis and provide a novel analytical platform for other diseases.

COPD is a devastating respiratory condition that manifests via persistent inflammation, emphysema development and small airway remodelling. Lung regeneration is defined as the ability of the lung to repair itself after injury by the proliferation and differentiation of progenitor cell populations, and becomes impaired in the COPD lung as a consequence of cell intrinsic epithelial stem cell defects and signals from the micro-environment. Although the loss of structural integrity and lung regenerative capacity are critical for disease progression, our understanding of the cellular players and molecular pathways that hamper regeneration in COPD remains limited. Intriguingly, despite being a key driver of COPD pathogenesis, the role of the immune system in regulating lung regenerative mechanisms is understudied. In this review, we summarise recent evidence on the contribution of immune cells to lung injury and regeneration. We focus on four main axes: 1) the mechanisms via which myeloid cells cause alveolar degradation; 2) the formation of tertiary lymphoid structures and the production of autoreactive antibodies; 3) the consequences of inefficient apoptotic cell removal; and 4) the effects of innate and adaptive immune cell signalling on alveolar epithelial proliferation and differentiation. We finally provide insight on how recent technological advances in omics technologies and human ex vivo lung models can delineate immune cell-epithelium cross-talk and expedite precision pro-regenerative approaches toward reprogramming the alveolar immune niche to treat COPD.

Autoimmunity plays a role in certain types of lung fibrosis, notably connective tissue disease-associated interstitial lung disease (CTD-ILD). In idiopathic pulmonary fibrosis (IPF), an incurable and fatal lung disease, diagnosis typically requires clinical exclusion of autoimmunity. However, autoantibodies of unknown significance have been detected in IPF patients. We conducted computational analysis of B cell transcriptomes in published transcriptomics datasets and developed a proteomic Differential Antigen Capture (DAC) assay that captures plasma antibodies followed by affinity purification of lung proteins coupled to mass spectrometry. We analyzed antibody capture in two independent cohorts of IPF and CTL-ILD patients over two disease progression time points. Our findings revealed significant upregulation of specific immunoglobulins with V-segment bias in IPF across multiple cohorts. We identified a predictive autoimmune signature linked to reduced transplant-free survival in IPF, persisting over time. Notably, autoantibodies against thrombospondin-1 were associated with decreased survival, suggesting their potential as predictive biomarkers.

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.